How to tune this model with this PSO coding? Is there any mistake in this coding or do I need to add something?

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Areen Dalina
Areen Dalina 2022-6-23
评论： Sam Chak 2022-6-23
Below is the NARX integrated with PSO but i cant tune PSO with NARX.
clear;
%load input-output data
tt=cputime;
load ui.dat; %load input
u=ui(:,1);
mx=5;
y(1:mx+1)=0;
for t=mx+1:500
for i=1:500
rand('twister',sum(100*clock));
e(i)=(rand-0.5)*0.002;
end
y(t)=0.797*y(t-1)-0.237*y(t-3)+0.441*u(t-1)+0.105*y(t-4)*u(t-4)...
+0.333*u(t-3)*u(t-5)+e(t);
% y(t)=1.5*y(t-1)-0.7*y(t-2)+u(t-5)+0.5*u(t-6);%+e(t);
% y(t)=0.5*y(t-1)-0.45*y(t-3)+1.2*u(t-3)+0.1*u(t-4);%+e(t); %Simulation 3
% y(t)=0.3*u(t-1)*u(t-2)-0.8*y(t-2)*u(t-1)*u(t-1)-0.1*u(t-1)*u(t-1)*u(t-2);%+e(t);
end
y=y';%load output
nodata=500;
noy=5; nou=5;
nl=2; start=max(nou,noy)+1;
m=nou+noy;
mphi=[]; %form regressor for ARMAX model
for i=start:nodata,
phi=[];
if nl==1
for j=1:noy,
phi=[phi y(i-j)];
end
for k=0:nou,
phi=[phi u(i-k)];
end;
end
if nl==2
for j=1:noy,
phi=[phi y(i-j)];
end
for k=0:nou,
phi=[phi u(i-k)];
end;
for j=1:noy,
for l=j:noy,
phi=[phi y(i-j)*y(i-l)];
end;
for k=1:nou,
phi=[phi y(i-j)*u(i-k)];
end;
end;
for k=1:nou,
for n=k:nou;
phi=[phi u(i-k)*u(i-n)];
end;
end;
end;
if nl==3
for j=1:noy,
phi=[phi y(i-j)];
end
for k=0:nou,
phi=[phi u(i-k)];
end;
for j=1:noy,
for l=j:noy,
phi=[phi y(i-j)*y(i-l)];
end;
for k=1:nou,
phi=[phi y(i-j)*u(i-k)];
end;
end;
for k=1:nou,
for n=k:nou;
phi=[phi u(i-k)*u(i-n)];
end;
end;
for j=1:noy,
for l=j:noy,
for p=l:noy
phi=[phi y(i-j)*y(i-l)*y(i-p)];
end;
for k=1:nou
phi=[phi y(i-j)*y(i-l)*u(i-k)];
end;
end;
for k=1:nou,
for n=k:nou,
phi=[phi y(i-j)*u(i-k)*u(i-n)];
end;
end;
end;
for k=1:nou,
for n=k:nou;
for q=n:nou
phi=[phi u(i-k)*u(i-n)*u(i-q)];
end
end;
end;
end;
mphi=[mphi;phi]; %end regressor
end;
[temp m]=size(mphi)
%% Initialization
%CONTROL PARAMETERS
clear
clc
n = 50; % Size of the swarm " no of birds "
bird_step = 50; % Maximum number of "birds steps"
dim = 10; % Dimension of the problem
c2 =1.2; % PSO parameter C1
c1 = 0.12; % PSO parameter C2
w =0.9; % pso momentum or inertia
fitness=0*ones(n,bird_step); %fitness population
%**********************************%
% Initialize the parameter
%**********************************%
R1 = rand(dim, n);
R2 = rand(dim, n);
current_fitness =0*ones(1,n);
%****************************************************%
% Initializing swarm and velocities and position
%****************************************************%
%Initialize random number of generator
current_position = 10*(rand(dim, n)-.5);
velocity = .3*randn(dim, n) ;
local_best_position = current_position ;
%**********************************%
% Evaluate initial population
%**********************************%
for i = 1:n
current_fitness(i) = rosenbrock(current_position(:,i));
end
local_best_fitness = current_fitness ;
[global_best_fitness,g] = min(local_best_fitness) ;
for i=1:n
globl_best_position(:,i) = local_best_position(:,g) ;
end
%**********************************%
% Optimization
%**********************************%
%------VELOCITY UPDATE------%
velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position));
%------SWARMUPDATE------%
current_position = current_position + velocity ;
%------EVALUATE NEW SWARM------%
global_best_fitness = inf;
%% MAIN LOOP %%
iter = 0 ; % Iterationscounter
while ( iter < bird_step )
iter = iter + 1;
for i = 1:n,
current_fitness(i) = rosenbrock(current_position(:,i)) ;
end
for i = 1 : n
if current_fitness(i) < local_best_fitness(i)
local_best_fitness(i) = current_fitness(i);
local_best_position(:,i) = current_position(:,i) ;
end
end
[current_global_best_fitness,g] = min(local_best_fitness);
if current_global_best_fitness < global_best_fitness
global_best_fitness = current_global_best_fitness;
for i=1:n
globl_best_position(:,i) = local_best_position(:,g);
end
end
velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position));
current_position = current_position + velocity;
x=current_position(1,:);
y=current_position(2,:);
clf
plot(x, y , 'h')
axis([-5 5 -5 5]);
pause(.3)
end % end of while loop its mean the end of all step that the birds move it
[Jbest_min,I] = min(current_fitness) % minimum fitness
current_position(:,I) % best solution
%
Below is the data input under name ui.dat
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回答（1 个）

Sam Chak
Sam Chak 2022-6-23
Your code is quite long. By the way, you are using Rosenbrock function as your fitness function, not your NARX model.
for i = 1:n
current_fitness(i) = rosenbrock(current_position(:,i));
end
  5 个评论
显示 3更早的评论
Paul
Paul 2022-6-23
Hi @Sam Chak,
I'm afraid I can't help with this question.
I did notice this line
current_position = current_position + velocity;
which reads a bit odd. Perhaps "velocity" in this context means "change in position"?
Sam Chak
Sam Chak 2022-6-23
No worries, @Paul. Thanks for checking out the question.
Regarding this:
current_position = current_position + velocity;
I have checked the particle swarm algorithm in MATLAB doc:
Another source also says the same thing:
I guess the update theory is similar to the Calculus or Finite Difference:
dx/dt = v
dx = v*dt
dx = x2 - x1
x2 = x1 + v*dt

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